Artificial Intelligence Supported Identification of Novel Non-invasive Biomarkers of Aging
It has been little bit over a 6-month period since we started the project website. Early stats indicate that there is an interest in the project. The statistcs has already recorded over 1000 visitors. Lots of this is thanks to the participation in live events and conferences, social media, and inter-project collaboration.
The Faculty of Medicine at the University of Montenegro is at the forefront of tackling some of the most pressing health challenges faced by the elderly population. Through innovative research and collaboration, the Faculty is driving progress in understanding and addressing chronic diseases associated with aging.
đ Key Projects:
1ïžâŁ AI-AGE Project
Launching in 2024, the Artificial Intelligence Supported Identification of Novel Non-Invasive Biomarkers of Aging aims to build scientific and innovation capacity in aging biomarkers. Supported by the Ministry of Science of Montenegro, this project utilizes cutting-edge AI technology to advance aging research.
2ïžâŁ RECOGNISED Project
Spanning from 2020 to 2024, the Retinal and Cognitive Dysfunction in Type 2 Diabetes project explores the links between retinal health, cognitive impairment, and dementia risk in individuals with type 2 diabetes. This initiative is pivotal in mitigating diabetes-related complications in aging populations. [link]
3ïžâŁ DEMONSTRATE Project
Running from 2019 to 2021, New Methods for Risk Stratification for Cancer and Alzheimerâs Disease Progression in Patients in Montenegro focused on innovative strategies to assess progression risks for these critical conditions.
âš Why It Matters:
These projects underscore the Facultyâs commitment to advancing medical research that improves healthcare outcomes for the elderly. By addressing chronic diseases and leveraging innovative approaches, the Faculty of Medicine is contributing to healthier aging and stronger healthcare systems.
đ Together, we are shaping a future where science and innovation meet to tackle the challenges of aging populations.
The scientific paper âDetection of Scoliosisâ by Elvis Taruh, Enisa Trubljanin, and Dejan BabiÄ explores the application of a deep learning model integrated with a web application to detect scoliosis using x-ray images. Utilizing a dataset of 198 x-ray images from Roboflow, the initial model performance was unsatisfactory, prompting manual annotation of 245 images, which significantly improved the modelâs accuracy. YOLOv8, a state-of-the-art object detection algorithm, was used to train two models, demonstrating improved performance with manual annotations. The web application, built with Flask, HTML, CSS, and JavaScript, provides a user-friendly interface for analyzing scoliosis detection results. The backend uses MySQL for data storage and management, facilitating efficient image processing, result display, and feedback from doctors. Evaluation metrics indicate that the second model, which underwent refined annotation and augmentation, performed better, avoiding overfitting and demonstrating higher precision. This approach enhances early scoliosis diagnosis and offers a scalable solution for other medical detection challenges, supporting healthcare providers with more accurate diagnostic tools and improving patient care.
AI-AGE research team received the ethical approaval to start research activites.
The private institution Faculty of Information Systems and Technologies, University of Donja Gorica (UDG) invites you to take part in the process of submitting bids for the selection of the most favorable supplier for the procurement of equipment for the Faculty of Information Systems and Technologies, UDG for the purposes of implementing âAI-AGE Artificial Intelligence in to the identification of new non-invasive biomarkers of agingâ, grant number 04-082/23-2528/1. The detailed information is made available at the following link.
Researchers from the AI-AGE team participate in the he 34th EUROPEAN ASSOCIATION FOR DIABETIC EYE COMPLICATIONS (EAsDEC) Meeting, which was hosted in the city of Milan, Italy. Initial research results from the AI-AGE project were presented in a form of a poster by prof. N Popovic and prof. A. Adzic Zecevic. The conference took place from 30 May to 1 June. More information can be found here.
ABSTRACT â Introduction / study design: Type 2 diabetes mellitus (T2D) is associated with changes in retinal microvascular complexity measured by fractal dimension (Df). Expression of micro RNAs (miRs), -146a and -101 is also affected by T2D, but the studies investigating these miRs in context of microvascular changes in T2D are scarce. Since hypertension (HTN) and Alzheimerâs dementia (AD) frequently coexist in patients with T2D, in the present cross-sectional observational prospective study, participants were divided in two groups â healthy (H, n=8), and with chronic disease (D, n=20, suffering from T2D, HTN and/or AD). The purpose: Study explores association between changes of retinal microvascular Df and expression levels of circulating miR-146a and miR-101 in patients with T2D. The influence of HTN and AD on this association is also investigated. Methods: Retinal fundus images were captured by using a non-mydriatic, hand-held MIIS-HORUS scope DES200. The optic disc-centered images were manually segmented, binarized, cropped to 350-pixel radius, and Df was determined by using ImageJ 1.53q. MiRs were isolated from plasma, quantified by qRT-PCR and normalized to expression levels of miR-361-5p. SPSS Statistics 29.0.1.0., t-test and ANCOVA were used to compare the two groups. P<0.05 was considered significant. Results: Age was not different between the 2 groups (Hvs.D mean age±SE=63.6±2.9 vs.68.5±1.8, p=0.17). Df and miR-101 expression were decreased in the group D (Hvs.D mean: Df±SE=1.36±0.01 vs.1.32±0.01, p=0.016; miR-101±SE=1.68±0.32 vs.0.83±0.22, p=0.041). Eight participants in the group D had T2D (1-moderate, and 7-no diabetic retinopathy). All participants with T2D had HTN, and 5 of them also had AD. Next, we used HTN and AD as covariates to account for effects of these comorbidities, and to determine effects of T2D. This analysis showed: in addition to decreased Df and miR-101 expression, T2D was associated with increased expression of miR-146a (Hvs.D mean miR-146a±SE=0.58±0.29 vs.1.69±0.17, p=0.018). Conclusions: fa Changes in Df and in expression of miR-101 are non-specific, and can be caused by T2D and concurrent comorbidities. Increased expression of miR-146a might be a part of the unique expression pattern of the circulatory miRNAs associated with T2D.
In April 2024, a delegation from the University of Donja Gorica (UDG) participated in the ASU-Cintana Presidentsâ Summit in Tempe, Arizona. The summit, hosted by Arizona State University (ASU) and Cintana Education, brought together university leaders, researchers, and industry experts to discuss global academic collaborations, innovation in higher education, and the role of AI-driven digital transformation.
Key Takeaways from the Summit
The event featured distinguished speakers, including Michael Crow (President, ASU), Julia Rosen (VP of Global Academic Initiatives, ASU), and Elizabeth Reilley (Executive Director, AI Acceleration, ASU). Discussions centered on emerging AI and ML applications in education, research, and healthcare, highlighting the need for interdisciplinary approaches to solve global challenges.
For UDG, this summit was an opportunity to strengthen ties with ASU and explore collaborative initiatives in AI-driven medical research, aligning with ongoing projects like AI-AGE. This project focuses on leveraging AI/ML techniques for medical applications, particularly in biomarker discovery and aging-related research.
AI-AGE and the Future of AI in Medicine
The AI-AGE project, which investigates deep learning for analyzing retinal images to identify biomarkers of aging, directly benefits from insights gained at the summit. ASUâs AI Acceleration initiatives and global partnerships set a benchmark for developing AI-driven solutions that are scalable and impactful in healthcare and medical diagnostics.
Through its participation in the ASU-Cintana network, UDG is positioning itself at the forefront of AI research in medicine, ensuring that its work in computer vision, deep learning, and medical data analysis contributes to the global conversation on AI for healthcare transformation.
Looking Ahead
The collaboration between UDG, ASU, and Cintana opens doors for joint research, academic exchanges, and industry-driven AI applications. One of the first steps in collaboration is creation of dual degrees between UDG and ASU. One of the first steps will be the creation of dual degree programs between the Faculty for information systems and techologies and ASU, which is the key for advancing the FIST/UDG as institution. As AI-AGE advances, UDG remains committed to pioneering innovative AI solutions for medicine, leveraging global partnerships to ensure impactful research and real-world applications.
A very successful two-day workshop for students and industry took place on April 22nd and 23rd 2023, on which young researches from AI-AGE gave presentation. The workshop was organized in the context of the training project called âCompetency Training for IoT and AI â InnovateYourFutureâ supported by ANSO â Alliance of International Science Organizations, China. The organization is done in collaboration with EuroCC Montenegro and Montenegrin AI Association. During the event, UDG also presented AI-AGE project, as well as some of other projects currently implemented at UDG.
Mr Dejan Babic gave presentation on ML/AI in medicine
AI-AGE project was presented at the 28th IEEE IT2024 Conference that took place 21-24 Feb 2024 in Zabljak, Montenegro. The presentation took place in a poster section dedicated to project presentations. We had a chance to talk to conference attendees, our colleauges from Montenegro and abroad. Also, the next day we had a large group of students participating in the EuroCC Workshop on HPC and Industry Applications, where we had a chance to discuss the project posters and take their attention to AI-AGE and other projects presented at the conference.
